{"id":"W1969973065","doi":"10.1186/1471-2105-15-11","title":"kruX: matrix-based non-parametric eQTL discovery","year":2014,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Biotechnology and Biological Sciences Research Council","keywords":"Expression quantitative trait loci; Parametric statistics; Computer science; Test statistic; Statistical hypothesis testing; Robustness (evolution); Quantitative trait locus; Multiple comparisons problem; Outlier; Computational biology; Biology; Genetics; Mathematics; Statistics; Artificial intelligence; Genotype; Single-nucleotide polymorphism; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005354997,0.0001749776,0.0002231147,0.0001050859,0.0001024972,0.0000493378,0.0002405038,0.0002426106,0.00001319296],"category_scores_gemma":[0.0006993099,0.0001532485,0.0001483857,0.0002025676,0.00006557535,0.000008715784,0.0000905393,0.00008995426,0.0001441726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002076038,"about_ca_system_score_gemma":0.0001202274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001335388,"about_ca_topic_score_gemma":0.00001827014,"domain_scores_codex":[0.998832,0.00006561338,0.0004496816,0.0001722858,0.0001337657,0.0003466083],"domain_scores_gemma":[0.9990176,0.00009882303,0.0002343657,0.0004841446,0.00007335478,0.00009174185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000258844,0.0005937567,0.6792175,0.0009318424,0.0003273849,0.000001379892,0.0002585708,0.1487093,0.00949869,0.002923391,0.1331466,0.02413274],"study_design_scores_gemma":[0.001755146,0.0006639454,0.1101649,0.00002340345,0.00006841445,0.00001077709,0.0001942928,0.816908,0.003731396,0.0003978571,0.06541524,0.0006666209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2587474,0.00006797582,0.7360374,0.0001044491,0.0002403264,0.0001787284,0.00002665595,0.00001986314,0.004577163],"genre_scores_gemma":[0.8051271,0.00003483944,0.1922258,0.0008532437,0.0002065825,0.00002170376,0.0002929309,0.00001898233,0.001218807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6681986,"threshold_uncertainty_score":0.6249293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01105009168094682,"score_gpt":0.2604783079881021,"score_spread":0.2494282163071553,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}